What are the hardware requirements for Gemma 4 26B A4B It AWQ 8bit?

Gemma 4 26B A4B It AWQ 8bit requires approximately 29.2 GB of VRAM. Quantized variants may run on less VRAM; see the Quantized Models section on this page.

Who developed Gemma 4 26B A4B It AWQ 8bit and how large is it?

Gemma 4 26B A4B It AWQ 8bit is developed by axteam, a model with 26b parameters. The model is published as open weights on Hugging Face and indexed on LLM Explorer with full benchmark history.

How does Gemma 4 26B A4B It AWQ 8bit perform on standard benchmarks?

Gemma 4 26B A4B It AWQ 8bit has the following published scores: LMSYS ELO 1438. Compare against reference models on this page or on the LLM Explorer leaderboards.

Gemma 4 26B A4B It AWQ 8bit by axteam — VRAM 29.2GB

Name: Gemma 4 26B A4B It AWQ 8bit
Author: axteam

Gemma 4 26B A4B It AWQ 8bit is an open-source language model by axteam. Features: 26b LLM, VRAM: 29.2GB, License: apache-2.0, Quantized, LLM Explorer Score: 0.42, ELO: 1438.

8bit Awq Base model:google/gemma-4-26b-... Base model:quantized:google/ge... Compressed-tensors Conversational Endpoints compatible Gemma Gemma4 Image-text-to-text Int8 Quantized Region:us Safetensors Sharded Tensorflow Vllm

Model Card on HF 🤗: https://huggingface.co/axteam/gemma-4-26B-A4B-it-AWQ-8bit

Gemma 4 26B A4B It AWQ 8bit Benchmarks

LMSys ELO 1438 vs 1272 (so35) 13.1%

^nn.n% — How the model compares to the reference models: Anthropic Sonnet 3.5 ("so35"), GPT-4o ("gpt4o") or GPT-4 ("gpt4").

What is the LLM Explorer Rank (Score)

Gemma 4 26B A4B It AWQ 8bit Parameters and Internals

LLM Name	Gemma 4 26B A4B It AWQ 8bit
Repository 🤗	https://huggingface.co/axteam/gemma-4-26B-A4B-it-AWQ-8bit
Base Model(s)	Gemma 4 26B A4B It google/gemma-4-26B-A4B-it
Model Size	26b
Required VRAM	29.2 GB
Updated	2026-10-15
Maintainer	axteam
Model Type	gemma4
Model Files	20.0 GB: 1-of-2 9.2 GB: 2-of-2
AWQ Quantization	Yes
Quantization Type	awq\|8bit
Model Architecture	Gemma4ForConditionalGeneration
License	apache-2.0
Transformers Version	5.5.0.dev0
Tokenizer Class	GemmaTokenizer
Padding Token	<pad>

Rank the Gemma 4 26B A4B It AWQ 8bit capabilities

Have you tried this model? Rate its performance — this feedback helps the ML community find the right model for their needs.

Instruction Following and Task Automation
Factuality and Completeness of Knowledge
Censorship and Alignment
Data Analysis and Insight Generation
Text Generation
Text Summarization and Feature Extraction
Code Generation
Multi-Language Support and Translation

Best Alternatives to Gemma 4 26B A4B It AWQ 8bit

Best Alternatives	Context / RAM	Downloads	Likes
Gemma 4 26B A4B It AWQ 4bit	0K / 17.3 GB	3069023	91
Gemma 4 26B A4B It AWQ 4bit	0K / 17.6 GB	75	1
Gemma 4 26B A4B AWQ 4bit	0K / 17.6 GB	64	1
Gemma 4 26B A4B AWQ 8bit	0K / 29.2 GB	33	1
...emma 4 26B A4B It Qat AWQ INT4	0K / 17.2 GB	231431	8
...x Imatrix3plus AWQ High6 Top40	0K / 15.5 GB	41	0
...6B A4B It Qat Q4 0 Unquantized	0K / 51.6 GB	33696	50
Gemma 4 26B A4b It 4bit	0K / 15.4 GB	27543	79
OTel 2.0 Decision 26B A4B 4bit	0K / 17.3 GB	63	1
...6B A4B It Qat Q4 0 Unquantized	0K / 51.6 GB	3487	3

Note: green Score (e.g. "73.2") means that the model is better than axteam/gemma-4-26B-A4B-it-AWQ-8bit.